CTNF 18/702,019 CTNF 85292 2475 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 2. Acknowledgment is made of this application is a National Stage Entry of PCT/JP2022/038759 filed on October 18, 2022, which claims priority from Japanese Patent Application 2021-182105 filed on November 8, 2021. Information Disclosure Statement 3. Acknowledgment is made of applicant's submission of Information Disclosure Statement, dated May 16, 2014. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Preliminary Amendment 4. Acknowledgment is made of Applicant’s submission of the preliminary amendment on April 17, 2024. Claims 3-7, 10, 13, 16, and 21 have been amended; claims 18 and 22-36 have been canceled. Upon entering the amendment, claims 1-17 and 19-21 remain pending. This communication is considered fully responsive and sets forth below. Specification 5. The title of the invention is not descriptive. The title of this application is “RADIO TERMINAL, RADIO ACCESS NETWORK NODE, AND METHODS THEREFOR.” The examiner objects the usage of this title as it is not clearly indicative of the invention to which the claims are directed. A substitution of a new title is anticipated. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 6. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 7. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA 8. Claim s 1-6, 10-17, and 19-21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al. (US 2024/0205781) . Regarding claim 1, Li et al. teach the radio terminal (paragraphs [0210] lines 1-10 & [0313] lines 1-12; Examiner’s Notes: UE depicted in FIG. 12 and hardware 1700 depicted in FIG. 17 of the prior art teaches the limitation of “radio terminal” in the instant application) comprising: at least one memory (paragraph [0313] lines 1-12; Examiner’s Notes: memory 1720 in hardware 1700 depicted in FIG. 17 of the prior art teaches the limitation of “one memory” in the instant application); and at least one processor (paragraph [0313] lines 1-12; Examiner’s Notes: processor 1710 in hardware 1700 depicted in FIG. 17 of the prior art teaches the limitation of “one processor” in the instant application) coupled to the at least one memory and configured to transmit assistance information to a network (paragraphs [0210] lines 1-10 & [0214] lines 1-5; Examiner’s Notes: training data depicted in FIG. 12 and FIG. 13 of the prior art teaches the limitation of “assistance information;” in fact, transmitting training data to the network, e.g., RAN, depicted in FIG. 13 of the prior art teaches the limitation of “transmit assistance information to a network” in the instant application), wherein the assistance information is used to train a first machine learning model related to radio access network optimization or to perform inference on a first trained machine learning model related to radio access network optimization (paragraph [0214] lines 1-5; Examiner’s Notes: the model training depicted in FIG. 13 of the prior art teaches the limitation of “a first trained machine learning model;” in fact, using the training data to train the model training regards to the RAN intelligence/optimization, as depicted in FIG. 13 of the prior art teaches the limitation of “wherein the assistance information is used to train a first machine learning model related to radio access network optimization” in the instant application; consequently, the cited paragraph teaches the limitation of “wherein the assistance information is used to train a first machine learning model related to radio access network optimization or to perform inference on a first trained machine learning model related to radio access network optimization” in the instance application as well), the assistance information includes one or both of statistical data and predicted data (paragraph [0215] lines 1-15; Examiner’s Notes: the data collection depicted in FIG. 13 of the prior art teaches the limitation of “statistical data;” in fact, the training data including the data collection, as depicted in FIG. 13 of the prior art teaches the limitation of “the assistance information includes one or both of statistical data and predicted data” in the instant application), the statistical data indicates a subset of historical data of a given event, extracted by filtering the historical data by one or more conditions (paragraph [0348] lines 1-7; Examiner’s Notes: the information/data, e.g., a statistic of mobility events, in the prior art teaches the limitation of “a subset of historical data of a given event;” in fact, the information/data indicating the statistic of mobility events in the prior art teaches the limitation of “the statistical data indicates a subset of historical data of a given event, extracted by filtering the historical data by one or more conditions” in the instant application), and the predicted data is generated by the radio terminal by prediction or decision using a second trained machine learning model (paragraph [0215] lines 1-15; Examiner’s Notes: the model inference depicted in FIG. 13 of the prior art teaches the limitation of “a second trained machine learning model;” the output depicted in FIG. 13 of the prior art teaches the limitation of “the predicted data;” in fact, generating the output using the model inference, as depicted in FIG. 13 of the prior art teaches the limitation of “the predicted data is generated by the radio terminal by prediction or decision using a second trained machine learning model” in the instant application). Regarding claim 2, Li et al. further teach the radio terminal , wherein the historical data indicates a history of success or failure of mobility or beam selection by the radio terminal (paragraphs [0107-0111]; Examiner’s Notes: the information/data on positioning and beam management regards to the UE in the prior art teaches the limitation of “wherein the historical data indicates a history of success or failure of mobility or beam selection by the radio terminal” in the instant application). Regarding claim 3, Li et al. further teach the radio terminal , wherein the one or more conditions include one or more conditions related to one or more of location, movement speed, serving beam, serving cell, and serving frequency band of the radio terminal (paragraphs [0107-0111]; Examiner’s Notes: the information/condition on positioning regards to the UE in the prior art teaches the limitation of “wherein the one or more conditions include one or more conditions related to one or more of location, movement speed, serving beam, serving cell, and serving frequency band of the radio” in the instant application). Regarding claim 4, Li et al. further teach the radio terminal , wherein the one or more conditions include one or more conditions related to a movement pattern of the radio terminal (paragraphs [0107-0111]; Examiner’s Notes: the information/condition on positioning regards to the UE in the prior art teaches the limitation of “wherein the one or more conditions include one or more conditions related to a movement pattern of the radio terminal” in the instant application). Regarding claim 5, Li et al. further teach the radio terminal , wherein the one or more conditions include one or more conditions related to a network slice used by the radio terminal (paragraph [0282] lines 1-14; Examiner’s Notes: the information/condition regards to a network element/slice, e.g., 1520 illustrated in FIG. 15 in the prior art teaches the limitation of “wherein the one or more conditions include one or more conditions related to a network slice used by the radio terminal” in the instant application). Regarding claim 6, Li et al. further teach the radio terminal , wherein the one or more conditions include one or more conditions related to a radio configuration of the radio terminal (paragraph [0057] lines 1-7; Examiner’s Notes: the information/condition indicating the configuration of the UE in the prior art teaches the limitation of “wherein the one or more conditions include one or more conditions related to a radio configuration of the radio terminal” in the instant application). Regarding claim 10, Li et al. further teach the radio terminal , wherein the predicted data is recorded data (paragraph [0025] lines 1-26; Examiner’s Notes: the recorded UE location in the prior art teaches the limitation of “recorded data;” in fact, the output/predicted data regards to the recorded UE location in the prior art teaches the limitation of “wherein the predicted data is recorded data” in the instant application). Regarding claim 11, Li et al. further teach the radio terminal , wherein the recorded data indicates a mobility execution timing as predicted or determined using the second trained machine learning model (paragraph [0072] lines 1-10; Examiner’s Notes: the recorded information/data indicating/predicting the UE moving speed in the prior art teaches the limitation of “wherein the recorded data indicates a mobility execution timing as predicted or determined using the second trained machine learning model” in the instant application). Regarding claim 12, Li et al. further teach the radio terminal , wherein the recorded data indicates a downlink beam determined by beam selection using the second trained machine learning model (paragraph [0293] lines 1-18; Examiner’s Notes: the information/data indicating the downlink beam based on the control policy in the prior art teaches the limitation of “wherein the recorded data indicates a downlink beam determined by beam selection using the second trained machine learning model” in the instant application). Regarding claim 13, Li et al. further teach the radio terminal , wherein the at least one processor is configured to: receive control information from the network (paragraph [0293] lines 1-18; Examiner’s Notes: receiving the control policy from the RAN in the prior art teaches the limitation of “receive control information from the network” in the instant application); and transmit to the network the assistance information indicating the predicted data based on the results of the prediction or decision using the second trained machine learning model, if the control information allows (paragraph [0214] lines 1-5; Examiner’s Notes: transmitting to the RAN the training data indicating the feedback based on the output, according to the control policy, as illustrated in FIG. 13 in the prior art teaches the limitation of “transmit to the network the assistance information indicating the predicted data based on the results of the prediction or decision using the second trained machine learning model, if the control information allows” in the instant application). Regarding claim 14, Li et al. further teach the radio terminal , wherein the control information indicates a condition under which transmission of the assistance information indicating the predicted data is allowed (paragraph [0293] lines 1-18; Examiner’s Notes: the control policy indicating the output/predicted data, as illustrated in FIG. 13 in the prior art teaches the limitation of “wherein the control information indicates a condition under which transmission of the assistance information indicating the predicted data is allowed” in the instant application), and the at least one processor is configured to transmit the assistance information indicating the predicted data to the network only if the condition is met (paragraph [0214] lines 1-5; Examiner’s Notes: transmitting to the RAN the training data indicating the output/predicted data according to the control policy, as illustrated in FIG. 13 in the prior art teaches the limitation of “transmit the assistance information indicating the predicted data to the network only if the condition is met” in the instant application). Regarding claim 15, Li et al. further teach the radio terminal , wherein the control information indicates a condition under which collection of the predicted data is allowed (paragraph [0293] lines 1-18; Examiner’s Notes: the control policy indicating the output/predicted data, as illustrated in FIG. 13 in the prior art teaches the limitation of “wherein the control information indicates a condition under which collection of the predicted data is allowed” in the instant application), and the at least one processor is configured to include in the assistance information only predicted data obtained when the condition is met (paragraph [0214] lines 1-5; Examiner’s Notes: transmitting to the RAN the training data according to the control policy, as illustrated in FIG. 13 in the prior art teaches the limitation of “include in the assistance information only predicted data obtained when the condition is met” in the instant application). Regarding claim 16, Li et al. further teach the radio terminal , wherein the condition indicates a restriction with respect to at least one of a frequency band, a location, or a time (paragraphs [0107-0111]; Examiner’s Notes: the information/condition indicating the positioning regards to the UE in the prior art teaches the limitation of “wherein the condition indicates a restriction with respect to at least one of a frequency band, a location, or a time” in the instant application). Regarding claim 17, Li et al. teach the method performed by a radio terminal (paragraphs [0210] lines 1-10 & [0313] lines 1-12; Examiner’s Notes: UE depicted in FIG. 12 and hardware 1700 depicted in FIG. 17 of the prior art teaches the limitation of “radio terminal” in the instant application) , the method comprising: transmitting assistance information to a network (paragraphs [0210] lines 1-10 & [0214] lines 1-5; Examiner’s Notes: training data depicted in FIG. 12 and FIG. 13 of the prior art teaches the limitation of “assistance information;” in fact, transmitting training data to the network, e.g., RAN, depicted in FIG. 13 of the prior art teaches the limitation of “transmitting assistance information to a network” in the instant application), wherein the assistance information is used to train a first machine learning model related to radio access network optimization or to perform inference on a first trained machine learning model related to radio access network optimization (paragraph [0214] lines 1-5; Examiner’s Notes: the model training depicted in FIG. 13 of the prior art teaches the limitation of “a first trained machine learning model;” in fact, using the training data to train the model training regards to the RAN intelligence/optimization, as depicted in FIG. 13 of the prior art teaches the limitation of “wherein the assistance information is used to train a first machine learning model related to radio access network optimization” in the instant application; consequently, the cited paragraph teaches the limitation of “wherein the assistance information is used to train a first machine learning model related to radio access network optimization or to perform inference on a first trained machine learning model related to radio access network optimization” in the instance application as well), the assistance information includes one or both of statistical data and predicted data (paragraph [0215] lines 1-15; Examiner’s Notes: the data collection depicted in FIG. 13 of the prior art teaches the limitation of “statistical data;” in fact, the training data including the data collection, as depicted in FIG. 13 of the prior art teaches the limitation of “the assistance information includes one or both of statistical data and predicted data” in the instant application), the statistical data indicates a subset of historical data of a given event, extracted by filtering the historical data by one or more conditions (paragraph [0348] lines 1-7; Examiner’s Notes: the information/data, e.g., a statistic of mobility events, in the prior art teaches the limitation of “a subset of historical data of a given event;” in fact, the information/data indicating the statistic of mobility events in the prior art teaches the limitation of “the statistical data indicates a subset of historical data of a given event, extracted by filtering the historical data by one or more conditions” in the instant application), and the predicted data is generated by the radio terminal by prediction or decision using a second trained machine learning model (paragraph [0215] lines 1-15; Examiner’s Notes: the model inference depicted in FIG. 13 of the prior art teaches the limitation of “a second trained machine learning model;” the output depicted in FIG. 13 of the prior art teaches the limitation of “the predicted data;” in fact, generating the output using the model inference, as depicted in FIG. 13 of the prior art teaches the limitation of “the predicted data is generated by the radio terminal by prediction or decision using a second trained machine learning model” in the instant application). Regarding claim 19, Li et al. teach the radio access network node (paragraphs [0210] lines 1-10 & [0313] lines 1-12; Examiner’s Notes: RAN, e.g., hardware 1700 depicted in FIG. 17 of the prior art teaches the limitation of “radio access network node” in the instant application) comprising: at least one memory (paragraph [0313] lines 1-12; Examiner’s Notes: memory 1720 in hardware 1700 depicted in FIG. 17 of the prior art teaches the limitation of “one memory” in the instant application); and at least one processor (paragraph [0313] lines 1-12; Examiner’s Notes: processor 1710 in hardware 1700 depicted in FIG. 17 of the prior art teaches the limitation of “one processor” in the instant application) coupled to the at least one memory and configured to receive assistance information from a radio terminal (paragraphs [0210] lines 1-10 & [0214] lines 1-5; Examiner’s Notes: UE depicted in FIG. 12 of the prior art teaches the limitation of “a radio terminal;” training data depicted in FIG. 12 and FIG. 13 of the prior art teaches the limitation of “assistance information;” in fact, receiving training data from UE, as depicted in FIG. 13 of the prior art teaches the limitation of “receive assistance information from a radio terminal” in the instant application), wherein the assistance information is used to train a first machine learning model related to radio access network optimization or to perform inference on a first trained machine learning model related to radio access network optimization (paragraph [0214] lines 1-5; Examiner’s Notes: the model training depicted in FIG. 13 of the prior art teaches the limitation of “a first trained machine learning model;” in fact, using the training data to train the model training regards to the RAN intelligence/optimization, as depicted in FIG. 13 of the prior art teaches the limitation of “wherein the assistance information is used to train a first machine learning model related to radio access network optimization” in the instant application; consequently, the cited paragraph teaches the limitation of “wherein the assistance information is used to train a first machine learning model related to radio access network optimization or to perform inference on a first trained machine learning model related to radio access network optimization” in the instance application as well), the assistance information includes one or both of statistical data and predicted data (paragraph [0215] lines 1-15; Examiner’s Notes: the data collection depicted in FIG. 13 of the prior art teaches the limitation of “statistical data;” in fact, the training data including the data collection, as depicted in FIG. 13 of the prior art teaches the limitation of “the assistance information includes one or both of statistical data and predicted data” in the instant application), the statistical data indicates a subset of historical data of a given event, extracted by filtering the historical data by one or more conditions (paragraph [0348] lines 1-7; Examiner’s Notes: the information/data, e.g., a statistic of mobility events, in the prior art teaches the limitation of “a subset of historical data of a given event;” in fact, the information/data indicating the statistic of mobility events in the prior art teaches the limitation of “the statistical data indicates a subset of historical data of a given event, extracted by filtering the historical data by one or more conditions” in the instant application), and the predicted data is generated by the radio terminal by prediction or decision using a second trained machine learning model (paragraph [0215] lines 1-15; Examiner’s Notes: the model inference depicted in FIG. 13 of the prior art teaches the limitation of “a second trained machine learning model;” the output depicted in FIG. 13 of the prior art teaches the limitation of “the predicted data;” in fact, generating the output using the model inference, as depicted in FIG. 13 of the prior art teaches the limitation of “the predicted data is generated by the radio terminal by prediction or decision using a second trained machine learning model” in the instant application). Regarding claim 20, Li et al. further teach the radio access network node , wherein the historical data indicates a history of success or failure of mobility or beam selection by the radio terminal (paragraphs [0107-0111]; Examiner’s Notes: the information/data on positioning and beam management regards to the UE in the prior art teaches the limitation of “wherein the historical data indicates a history of success or failure of mobility or beam selection by the radio terminal” in the instant application). Regarding claim 21, Li et al. further teach the radio access network node , wherein the one or more conditions include one or more conditions related to one or more of location, movement speed, serving beam, serving cell, and serving frequency band of the radio terminal (paragraphs [0107-0111]; Examiner’s Notes: the information/condition on positioning regards to the UE in the prior art teaches the limitation of “wherein the one or more conditions include one or more conditions related to one or more of location, movement speed, serving beam, serving cell, and serving frequency band of the radio” in the instant application) . Claim Rejections - 35 USC § 103 07-20 AIA 9. The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action: (a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made. 07-23 AIA 10. The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21 AIA 11. Claim s 7-9 are rejected under 35 U.S.C. 103(a) as being unpatentable over Li et al. (US 2024/0205781) in view of Svennebring et al. (US 2023/0308199) . Regarding claim 7, Li et al. teach the radio terminal without explicitly teaching wherein the predicted data is real-time data that is sent to the network in response to the predicted data being generated. Svennebring et al. from the same or similar field of endeavor teach implementing fairness of the method, wherein the predicted data is real-time data that is sent to the network in response to the predicted data being generated (paragraph [0025] lines 1-21; Examiner’s Notes: the real-time data regards to the prediction in the prior art teaches the limitation of “the predicted data is real-time data;” in fact, feeding back the real-time data to the RAN based on the prediction in the prior art teaches the limitation of “wherein the predicted data is real-time data that is sent to the network in response to the predicted data being generated” in the instant application), and the predicted data is used to perform inference on the first trained machine learning model (paragraph [0027] lines 1-11; Examiner’s Notes: learning/adapting the changes in the prior art teaches the limitation of “perform inference on the first trained machine learning model;” in fact, using the output/predicted data to learn/adapt the changes regards to the machine learning in the prior art teaches the limitation of “the predicted data is used to perform inference on the first trained machine learning model” in the instant application). Thus, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in art to implement the method of Svennebring et al. in the system of Li et al. The motivation for performing wherein the predicted data is real-time data that is sent to the network in response to the predicted data being generated, is to further enhance the mechanism for predicting future path of the mobile device based on the current path, which indicates a sequence of predicted future positions of the mobile device over a future time window, and a link performance prediction (LPP) is then generated for the mobile device based on the future path of the mobile device and a base station coverage map. Regarding claim 8, Svennebring et al. further teach the radio terminal, wherein the real-time data indicates a predicted location or trajectory of the radio terminal (paragraph [0036] lines 1-20; Examiner’s Notes: the real-time data indicating the client device information, e.g., location, in the prior art teaches the limitation of “wherein the real-time data indicates a predicted location or trajectory of the radio terminal” in the instant application). Regarding claim 9, Svennebring et al. further teach the radio terminal, wherein the real-time data indicates a predicted candidate target cell or candidate downlink beam (paragraph [0027] lines 1-11; Examiner’s Notes: the real-time data indicating a change regards to the predicted cell in the prior art teaches the limitation of “wherein the real-time data indicates a predicted candidate target cell or candidate downlink beam” in the instant application) . Conclusion 07-96 AIA 12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Davydov (US 2019/0149256) is cited for using channel state information (CSI) associated with a CSI interference measurement, wherein the CSI interference measurement is associated with an aggressor enhance Node B (eNB), the CSI is transmitted to the serving eNB, the CSI is then transmitted to the aggressor eNB over an X2 interface, the aggressor eNB determines beamforming restriction information based upon the CSI, the beamforming restriction information is then transmitted to user equipment served by the aggressor eNB, and the user equipment served by the aggressor eNB uses the beamforming restriction information in selecting channel state information reference signal resource indicator index. Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEI ZHAO whose telephone number is (571)270-5672. The examiner can normally be reached on 8-5. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JAE Y. LEE can be reached on 571-270-3936. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /WEI ZHAO/ Primary Examiner Art Unit 2479 Application/Control Number: 18/702,019 Page 2 Art Unit: 2479 Application/Control Number: 18/702,019 Page 3 Art Unit: 2479 Application/Control Number: 18/702,019 Page 4 Art Unit: 2479 Application/Control Number: 18/702,019 Page 5 Art Unit: 2479 Application/Control Number: 18/702,019 Page 6 Art Unit: 2479 Application/Control Number: 18/702,019 Page 7 Art Unit: 2479 Application/Control Number: 18/702,019 Page 8 Art Unit: 2479 Application/Control Number: 18/702,019 Page 9 Art Unit: 2479 Application/Control Number: 18/702,019 Page 10 Art Unit: 2479 Application/Control Number: 18/702,019 Page 11 Art Unit: 2479 Application/Control Number: 18/702,019 Page 12 Art Unit: 2479 Application/Control Number: 18/702,019 Page 13 Art Unit: 2479 Application/Control Number: 18/702,019 Page 14 Art Unit: 2479 Application/Control Number: 18/702,019 Page 15 Art Unit: 2479 Application/Control Number: 18/702,019 Page 16 Art Unit: 2479 Application/Control Number: 18/702,019 Page 17 Art Unit: 2479 Application/Control Number: 18/702,019 Page 18 Art Unit: 2479 Application/Control Number: 18/702,019 Page 19 Art Unit: 2479 Application/Control Number: 18/702,019 Page 20 Art Unit: 2479 Application/Control Number: 18/702,019 Page 21 Art Unit: 2479
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